Right-Size an Internal Model Route by Requests per User Before You Provision the Server

30,000,000 tokens breaks down to 1,000,000 tokens per day. At a routed generation of 2,800 tokens, that is 357 requests per day. Assign those requests across a 20-person product team and each person gets about 17 calls per day. That number, rather than the dashboard balance, decides whether an internal assistant can be rolled out to the whole team or remains a single-user experiment.

This is an overlooked layer in the current agent-tool conversation. Teams are building permission gates for tools, but many skip the capacity gate underneath: can the free model route support the actual user population without the first owner spending the entire allowance by Thursday? MonkeyCode's open-source project advertises a free model route and a free server option. The outreach brief for this article states that the current free tier includes a 30,000,000 monthly token allowance. I treat the 30M number as an operator-supplied claim to verify in the dashboard before relying on it. Disclosure: This article was prepared as part of MonkeyCode's product outreach.

The method here converts a token balance into requests per user, exposes that arithmetic as a small API, and uses the free server option as the host for that API. The model route remains free for generations; the calculator consumes no tokens.

Why a token balance hides the real constraint

A dashboard balance answers “how much is left.” It does not answer “can this team use the feature without one power user exhausting the shared allowance.” The difference matters when the route moves from a single developer's script to a shared internal service.

Planning view	Question it answers	Blind spot
Provider token balance	How many tokens remain in the cycle?	Request size variance, per-person distribution, peak demand
Requests per user per day	Can this group adopt the route?	Prompt drift toward longer inputs
Per-user quota policy	Who is blocked first when capacity is exhausted?	Shared-key workarounds
Synthetic workload on a candidate endpoint	How does it behave under modeled demand?	Real usage that diverges from the fixture

The free server option does not resolve those blind spots by itself. It gives you a place to run the deterministic budget API so the whole team sees the same decision instead of each person doing napkin math.

Convert the allowance into a repeatable calculator

The arithmetic is small enough to get wrong in a meeting. I move it into a script with four inputs: monthly tokens, users, tokens per request, and the desired requests per user per day. The output says whether the requested workload fits.

import argparse

def parse_args():
    parser = argparse.ArgumentParser()
    parser.add_argument('--monthly-tokens', type=int, default=30_000_000)
    parser.add_argument('--users', type=int, required=True)
    parser.add_argument('--tokens-per-request', type=int, default=2800)
    parser.add_argument('--requests-per-user-day', type=int, default=8)
    return parser.parse_args()

def main():
    args = parse_args()
    days = 30
    daily_requests = args.monthly_tokens / days / args.tokens_per_request
    per_user = daily_requests / args.users
    requested_tokens = args.users * args.requests_per_user_day * args.tokens_per_request * days
    headroom = args.monthly_tokens - requested_tokens
    print(f'daily_requests={daily_requests:.1f}')
    print(f'budget_per_user_per_day={per_user:.1f}')
    print(f'requested_tokens={requested_tokens:,}')
    print(f'headroom_tokens={headroom:,}')
    print(f'viable={requested_tokens <= args.monthly_tokens}')

if __name__ == '__main__':
    main()

Run it for a 20-person team at 10 calls per person per day:

python capacity_budget.py --users 20 --requests-per-user-day 10 --tokens-per-request 2800

The tool returns viable=True with headroom_tokens=13,200,000. That number is useful only because the input workload was made explicit. Once the workload is explicit, the team can negotiate which variable changes: fewer users, shorter prompts, or a lower calls-per-day target.

Expose the same math as an API on the free server

The command-line version works for one engineer. The API version works for an engineering group that keeps asking “can we add another integration?” If the free server plan allows a small Python process, deploy this FastAPI endpoint there:

from fastapi import FastAPI, HTTPException
from pydantic import BaseModel

class Budget(BaseModel):
    monthly_tokens: int = 30_000_000
    users: int
    tokens_per_request: int = 2800
    requests_per_user_day: int = 8
    days: int = 30

app = FastAPI()

@app.post('/budget')
def budget(body: Budget):
    daily_requests = body.monthly_tokens / body.days / body.tokens_per_request
    per_user = daily_requests / body.users
    requested_tokens = body.users * body.requests_per_user_day * body.tokens_per_request * body.days
    viable = requested_tokens <= body.monthly_tokens

    return {
        'daily_requests': round(daily_requests, 1),
        'per_user_per_day': round(per_user, 1),
        'requested_tokens': requested_tokens,
        'headroom_tokens': body.monthly_tokens - requested_tokens,
        'viable': viable,
    }

A colleague can ask the same question without opening a spreadsheet:

curl -X POST https://your-free-server/budget \
  -H 'Content-Type: application/json' \
  -d '{"users":20,"requests_per_user_day":10,"tokens_per_request":2800}'

This endpoint never calls the model route, so it does not spend generation tokens. If the request is not viable, return 429 from this endpoint or from the gateway so a bad rollout request fails before it burns the shared allowance:

if not viable:
    raise HTTPException(status_code=429, detail='requested workload exceeds free allowance')

Use a scenario table to catch capacity cliffs

The most dangerous scenario is not the one that obviously fails. It is the one that fits by a small margin. A 4% headroom plan will fail under prompt drift, reranking, or a teammate turning on a new tool.

Scenario	Users	Tokens/request	Calls/user/day	Monthly tokens	Fits 30M?	Headroom
Small eng team	6	2,800	8	4,032,000	yes	25,968,000
Full product team	20	2,800	8	13,440,000	yes	16,560,000
Heavy usage	20	2,800	20	33,600,000	no	-3,600,000
High-turnover support	50	1,400	10	21,000,000	yes	9,000,000

The full product team looks comfortable until someone moves from short classification prompts to longer summarization prompts. If that team's average request grows from 2,800 to 3,800 tokens, the same call schedule becomes 18,240,000 tokens per month. That still fits but shaves more than 4.5M tokens off the headroom. The scenario table should be rerun every time a prompt changes shape, not once at project setup.

Limitations and who should skip

A monthly token allowance is a current claim, not a permanent capacity target. Verify the number in the provider dashboard and do not build a hard quota from a third-party summary. Request sizes should come from measured percentiles, not the optimistic average; I use the 90th percentile tokens per request in the calculator when available.

The free server option may impose idle limits or cold starts, so verify that the plan can host a small always-available API before making it the source of truth for rollout decisions. If it cannot, the same endpoint can run locally or in a CI job. The value is the shared arithmetic, not the host.

Skip this approach if you have dedicated billing, per-project budgets, and an existing cost model. Skip it too when the free tier is a temporary learning sandbox rather than a shared production dependency. The calculator is for teams deciding whether a free route can become a shared internal service without silently exhausting the allowance.

At what user count did your free route stop looking free: ten, twenty, or the first time someone integrated it into a batch job?